Feature Engineering & Selection vs. Representation Learning

[gtesei]

I'm a big fun of Applied Predictive Modeling and writing a book entirely on feature engineering & selection is an excellent opportunity to focus on a key activity of of the end-to-end process. I find the initial chapters very well done - as usual - and the fact that they are free online is really admirable. Hence, I'm providing an honest feedback regarding a key point which I think can be in the mind many potential readers. This point is about the overall motivation of feature engineering and selection.

Recently - e.g. I. Goodfellow and Y. Bengio and A. Courville, Deep Learning, 2016 (http://www.deeplearningbook.org/contents/intro.html ) - it is reported the distinction between "classical machine learning" vs. "representation learning", where the difference is that in the former approach features are hand designed by data scientists while in the latter they are learnt by machines (Figure 1.5)

"[...] One solution to this problem is to use machine learning to discover not only the mapping from representation to output but also the representation itself.This approach is known as representation learning. Learned representations often result in much better performance than can be obtained with hand-designed representations. They also enable AI systems to rapidly adapt to new tasks, with minimal human intervention. A representation learning algorithm can discover a good set of features for a simple task in minutes, or for a complex task in hours to months. Manually designing features for a complex task requires a great deal of human time and effort; it can take decades for an entire community of researchers. [..]"

I think the reader could be interested in your point of view.

[topepo]

Good point. I'll add something to the introduction.